Background: Adenosine monophosphate-activated protein kinase (AMPK) is associated with the development of liver hepatocellular carcinoma (LIHC). AMPKα2, an α2 subunit of AMPK, is encoded by PRKAA2, and functions as the catalytic core of AMPK. However, the role of AMPKα2 in the LIHC tumor immune environment is unclear. Methods: RNA-seq data were obtained from the Cancer Genome Atlas and Genotype-Tissue Expression databases. Using the single-cell RNA-sequencing dataset for LIHC obtained from the China National Genebank Database, the communication between malignant cells and T cells in response to different PRKAA2 expression patterns was evaluated. In addition, the association between PRKAA2 expression and T-cell evolution during tumor progression was explored using Pseudotime analysis, and the role of PRKAA2 in metabolic reprogramming was explored using the R “scMetabolis” package. Functional experiments were performed in LIHC HepG2 cells. Results: AMPK subunits were expressed in tissue-specific and substrate-specific patterns. PRKAA2 was highly expressed in LIHC tissues and was associated with poor patient prognosis. Tumors with high PRKAA2 expression displayed an immune cold phenotype. High PRKAA2 expression significantly promoted LIHC immune escape. This result is supported by the following evidence: 1) the inhibition of major histocompatibility complex class I (MHC-I) expression through the regulation of interferon-gamma activity in malignant cells; 2) the promotion of CD8+ T-cell exhaustion and the formation of CD4+ Treg cells in T cells; 3) altered interactions between malignant cells and T cells in the tumor immune environment; and 4) induction of metabolic reprogramming in malignant cells. Conclusions: Our study indicate that PRKAA2 may contribute to LIHC progression by promoting metabolic reprogramming and tumor immune escape through theoretical analysis, which offers a theoretical foundation for developing PRKAA2-based strategies for personalized LIHC treatment.

Currently, no clinically relevant non-invasive biomarkers are available for screening of multiple cancer types. In this study, we developed a serum diagnostic signature based on 5-methylcytosine (m5C)-related miRNAs (m5C-miRNAs) for multiple-cancer detection. Serum miRNA expression data and the corresponding clinical information of patients were collected from the Gene Expression Omnibus database. Serum samples were then randomly assigned to the training or validation cohort at a 1:1 ratio. Using the identified m5C-miRNAs, an m5C-miRNA signature for cancer detection was established using a support vector machine algorithm. The constructed m5C-miRNA signature displayed excellent accuracy, and its areas under the curve were 0.977, 0.934, and 0.965 in the training cohort, validation cohort, and combined training and validation cohort, respectively. Moreover, the diagnostic capability of the m5C-miRNA signature was unaffected by patient age or sex or the presence of noncancerous disease. The m5C-miRNA signature also displayed satisfactory performance for distinguishing tumor types. Importantly, in the detection of early-stage cancers, the diagnostic performance of the m5C-miRNA signature was obviously superior to that of conventional tumor biomarkers. In summary, this work revealed the value of serum m5C-miRNAs in cancer detection and provided a new strategy for developing non-invasive and cost effective tools for large-scale cancer screening.

The administration of mRNA-based tumour vaccines is considered a promising strategy in tumour immunotherapy, although its application against kidney renal clear cell carcinoma (KIRC) is still at its infancy stage. The purpose of this study was to identify potential antigens and to further select suitable patients for vaccination. Gene expression data and clinical information were retrieved from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. GEPIA2 was used to evaluate the prognostic value of selected antigens. The relationship of antigens presenting cell infiltration with antigen expression was evaluated by TIMER, and immune subtypes were determined using unsupervised cluster analysis. Tumour antigens LRP2 and DOCK8, which are associated with prognosis and tumour-infiltrating antigen-presenting cells, were identified in KIRC. A total of six immune subtypes were identified, and patients with immune subtype 1–4 (IS1–4) tumours had an immune ‘cold’ phenotype, a higher tumour mutation burden, and poor survival. Moreover, these immune subtypes showed significant differences in the expression of immune checkpoint and immunogenic cell death modulators. Finally, the immune landscape of KIRC revealed the immune-related cell components in individual patients. This study suggests that LRP2 and DOCK8 are potential KIRC antigens in the development of mRNA vaccines, and patients with immune subtypes IS1–4 are suitable for vaccination.